AHAModelCol
Introduction
We group CVD diagnostics according to the following ICD diagnostics
| CVD | Codes | Description.ICD10 |
|---|---|---|
| IHD | I20 | Angina pectoris |
| IHD | I21 | Acute myocardial infarction |
| IHD | I22 | Subsequent ST elevation and non-ST elevation myocardial infarction |
| IHD | I23 | Certain current complications following ST elevation and non-ST elevation myocardial infarction |
| IHD | I24 | Other acute ischemic heart diseases |
| IHD | I25 | Chronic ischemic heart disease |
| HSTR | I60 | Nontraumatic subarachnoid hemorrhage |
| HSTR | I61 | Nontraumatic intracerebral hemorrhage |
| HSTR | I62 | Other and unspecified nontraumatic intracranial hemorrhage |
| ISTR | I63 | Cerebral infarction |
| ISTR | I65 | Occlusion and stenosis of precerebral arteries, not resulting in cerebral infarction |
| ISTR | I66 | Occlusion and stenosis of cerebral arteries, not resulting in cerebral infarction |
| ISTR | I67 | Other cerebrovascular diseases |
| ISTR | I69 | Sequelae of cerebrovascular disease |
| HHD | I10 | Essential (primary) hypertension |
| HHD | I11 | Hypertensive heart disease |
| HHD | I12 | Hypertensive chronic kidney disease |
| HHD | I13 | Hypertensive heart and chronic kidney disease |
| HHD | I15 | Secondary hypertension |
| HHD | I16 | Hypertensive crisis |
Demography
Population projections
We use DANE’s official population projections for 2024 - 2050 instead UN-WPP median estimates. Here some descriptive statistics
Epidemiology
This section shows the prevalence and infection rates raw data and estimation for 2019.
Prevalence rates
We use claim microdata from the Contributory Regime to estimate prevalence rates for grouped causes. Patient with at least one claim with a Dx code in our ICD-10 is tagged. Prevalence numerator is the sum of tagged patients and the denomitator is the average insured people in a year.
We compare the estimates rates to the based rates of the published model. At a first sight, estimated rates are lower than model base rates:
| cause | year | sex | age_g | PREVt0 | Prevalencia |
|---|---|---|---|---|---|
| hhd | 2019 | Female | 06.45-49 | 0.5745585 | NA |
| hstroke | 2019 | Female | 06.45-49 | 2.7946350 | 0.2793662 |
| ihd | 2019 | Female | 06.45-49 | 7.9361730 | 4.3042557 |
| istroke | 2019 | Female | 06.45-49 | 7.3552770 | 2.2349297 |
| hhd | 2019 | Male | 06.45-49 | 0.4673075 | NA |
| hstroke | 2019 | Male | 06.45-49 | 1.7634460 | 0.2590727 |
| ihd | 2019 | Male | 06.45-49 | 12.1707700 | 6.3894737 |
| istroke | 2019 | Male | 06.45-49 | 5.2246100 | 1.8105483 |
| hhd | 2019 | Female | 07.50-54 | 1.0590450 | NA |
| hstroke | 2019 | Female | 07.50-54 | 3.3719310 | 0.4334084 |
| ihd | 2019 | Female | 07.50-54 | 13.3120400 | 7.1548391 |
| istroke | 2019 | Female | 07.50-54 | 9.6120520 | 3.1980072 |
| hhd | 2019 | Male | 07.50-54 | 0.9047500 | NA |
| hstroke | 2019 | Male | 07.50-54 | 2.3384350 | 0.3580290 |
| ihd | 2019 | Male | 07.50-54 | 21.6029100 | 11.7916084 |
| istroke | 2019 | Male | 07.50-54 | 7.0819480 | 2.8128629 |
| hhd | 2019 | Female | 08.55-59 | 1.4337430 | NA |
| hstroke | 2019 | Female | 08.55-59 | 4.0854590 | 0.4961360 |
| ihd | 2019 | Female | 08.55-59 | 21.8082900 | 11.8423493 |
| istroke | 2019 | Female | 08.55-59 | 12.4932400 | 4.5162287 |
| hhd | 2019 | Male | 08.55-59 | 1.2539510 | NA |
| hstroke | 2019 | Male | 08.55-59 | 3.1348710 | 0.4892804 |
| ihd | 2019 | Male | 08.55-59 | 36.6100900 | 21.4444606 |
| istroke | 2019 | Male | 08.55-59 | 9.8419170 | 4.4384721 |
| hhd | 2019 | Female | 09.60-64 | 2.1788340 | NA |
| hstroke | 2019 | Female | 09.60-64 | 4.8941650 | 0.7349283 |
| ihd | 2019 | Female | 09.60-64 | 34.3364000 | 18.5802021 |
| istroke | 2019 | Female | 09.60-64 | 16.2241900 | 6.4795227 |
| hhd | 2019 | Male | 09.60-64 | 2.1822150 | NA |
| hstroke | 2019 | Male | 09.60-64 | 4.0699090 | 0.8195751 |
| ihd | 2019 | Male | 09.60-64 | 58.7672500 | 35.6811124 |
| istroke | 2019 | Male | 09.60-64 | 13.8246200 | 6.8935372 |
| hhd | 2019 | Female | 10.65-69 | 4.9504330 | NA |
| hstroke | 2019 | Female | 10.65-69 | 5.5550260 | 0.9041591 |
| ihd | 2019 | Female | 10.65-69 | 54.0097300 | 27.9578745 |
| istroke | 2019 | Female | 10.65-69 | 21.3305100 | 9.2278138 |
| hhd | 2019 | Male | 10.65-69 | 6.1593800 | NA |
| hstroke | 2019 | Male | 10.65-69 | 4.7915950 | 1.0373262 |
| ihd | 2019 | Male | 10.65-69 | 93.7193900 | 51.5857942 |
| istroke | 2019 | Male | 10.65-69 | 19.3602500 | 10.6362462 |
| hhd | 2019 | Female | 11.70-74 | 9.4940200 | NA |
| hstroke | 2019 | Female | 11.70-74 | 5.8961590 | 1.1187470 |
| ihd | 2019 | Female | 11.70-74 | 78.8542100 | 37.6911193 |
| istroke | 2019 | Female | 11.70-74 | 28.0107300 | 13.2185286 |
| hhd | 2019 | Male | 11.70-74 | 13.1262300 | NA |
| hstroke | 2019 | Male | 11.70-74 | 4.9864260 | 1.3205901 |
| ihd | 2019 | Male | 11.70-74 | 137.1658000 | 66.8702411 |
| istroke | 2019 | Male | 11.70-74 | 26.2524300 | 15.6646574 |
| hhd | 2019 | Female | 12.75+ | 14.0410800 | NA |
| hhd | 2019 | Female | 12.75+ | 18.6395600 | NA |
| hstroke | 2019 | Female | 12.75+ | 5.9837230 | 1.3741792 |
| hstroke | 2019 | Female | 12.75+ | 5.9874500 | 1.3741792 |
| ihd | 2019 | Female | 12.75+ | 108.5482000 | 40.2666138 |
| ihd | 2019 | Female | 12.75+ | 139.5900000 | 40.2666138 |
| istroke | 2019 | Female | 12.75+ | 35.5508200 | 24.3758696 |
| istroke | 2019 | Female | 12.75+ | 42.6127700 | 24.3758696 |
| hhd | 2019 | Male | 12.75+ | 20.5636200 | NA |
| hhd | 2019 | Male | 12.75+ | 28.1359000 | NA |
| hstroke | 2019 | Male | 12.75+ | 4.5050420 | 1.5761452 |
| hstroke | 2019 | Male | 12.75+ | 3.7500910 | 1.5761452 |
| ihd | 2019 | Male | 12.75+ | 185.3376000 | 58.6850123 |
| ihd | 2019 | Male | 12.75+ | 230.2488000 | 58.6850123 |
| istroke | 2019 | Male | 12.75+ | 33.2883000 | 22.1376751 |
| istroke | 2019 | Male | 12.75+ | 38.8009600 | 22.1376751 |
Mortality rates
We use DANE’s official population estimates and death counts for 2015-2019 to estimate death probabilities by cause, age and sex. Here some descriptive statistics:
Male nqx
Economic evaluation
This section shows the cost raw data and estimation for 2019. We estimate the average cost per person in a year using claims data from peopled insured in the Contributory regime.
Female cost
## [1] "Exchange Rate USD/COP2019 3277"